青海玉树地震差分干涉雷达同震形变测量

2010-04-14青海玉树发生7.1级地震后,作者利用震前和震后获取的日本ALOS卫星PALSAR遥感数据,开展了差分干涉雷达(D-InSAR)地震同震形变测量与分析。结果表明:玉树地震引起较大范围地表变形,地震变形沿玉树—甘孜断裂带向南东东方向扩展,在N33.7°,E96.81°附近达到最大形变量,D-InSAR监测到雷达视向上的最大形变量为35cm。地表形变特征对于评价玉树地震破坏程度、推断断层性质、研究地震形变和地震孕育特征具有重要的参考价值。     

Study of the optimum time span for distinguishing <Emphasis Type="Italic">Avena ludoviciana</Emphasis> from wheat crop based on their spectral reflectance characteristics

The study to establish the optimum time span for distinguishing Avena ludoviciana from wheat crop based on their spectral signatures was carried out at Student’s Research Farm, Department of Agronomy during 2006–07 and 2007–08. The experimental sites during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experiment was laid out in randomized block design with four replications and consisting of twelve treatments comprising 0, 10, 15, 25, 50, 75, 100, 125, 150, 200, 250 plants m⁻² and a pure Avena ludoviciana plot (T_max). The results revealed that in all the treatments irrespective of wheat and weeds, the red reflectance (%) value decreased from 34 to 95 DAS (days after sowing) in 2006–07 and 45 DAS to 100 DAS during 2007–08, and thereafter a sharp increase was observed in all the treatments. This trend might be due to increased chlorophyll index after 34 DAS as red reflectance was reduced by chlorophyll absorption. Among all the treatments, T_max (Pure Avena ludoviciana plot) had the highest red reflectance and T₀ (Pure wheat plot) had a lowest value of red reflectance during both the years. The highest value of IR reflectance was obtained at 95 DAS (2006–07) and 70 DAS (2007–08) in all the treatments. IR reflectance of wheat crop ranged between 24.61 and 61.21 per cent during 2006–07 and 27.33 and 67.3 per cent during 2007–08. However, IR reflectance values declined after 95 DAS and 70 DAS up to harvesting during 2006–07 and 2007–08. This lower reflectance may have been due to the onset of senescence. The highest RR and NDVI values were recorded under pure wheat treatment and minimum under pure weed plots. This may be due to dark green colour and better vigor of the wheat as compared to Avena ludoviciana. It was observed that by using RR and NDVI, pure wheat can be distinguished from pure populations of Avena ludoviciana after 34 DAS and different levels of weed populations can be discriminated amongst themselves from 68 DAS up to 107 DAS during both the years of investigation.     

Variability analysis of groundwater levels — AGIS-based case study

The present study has analyzed the variability in depth to water level below ground level (bgl) vis-à-vis groundwater development and rainfall from 1987 to 2007 in agriculture dominated Kaithal district of Haryana state in India. Spatial distribution of groundwater depth was mapped and classified into different zones using ILWIS 3.6 GIS tools. Change detection maps were prepared for 1987–1997 and 1997–2007. Groundwater depletion rates during successive decades were compared and critical areas with substantial fall in groundwater levels were identified. Further, block wise trends of change in groundwater levels were also analyzed. The water table in fresh belt areas of the district (Gulha, Pundri and Kaithal blocks) was observed to decline by a magnitude ranging from 10 m to 23 m. In Kalayat and Rajaund blocks, the levels were found fluctuating in a relatively narrow range of 4–9 m. During 1997–2007, the depletion has been faster compared to the preceding decade. Excessive groundwater depletion in major part of the district may be attributed to indiscriminate abstraction for irrigation and decrease in rainfall experienced since 1998. Changes in cropping pattern and irrigation methods are needed in the study area for sustainable management of the resource.     

Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution

In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM). It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty.     

Land suitability studies for major crops in Pavagada taluk,Karnataka using remote sensing and GIS techniques

Pavagada taluk of Tumkur district in Karnataka is one of the most backward taluks receiving less than 500 mm annual rainfall. The maximum area of the taluk is under monocropping, reasons for the same were not documented well. The present study was carried out using remote sensing data along with field survey and laboratory analysis for assessing the potentials and limitations of soil. Using the basic information on soil, climate and topography based on the matching exercise between the growth and production requirements of the crop, suitability of soils for groundnut, paddy and finger millet was assessed as per FAO land evaluation. The soil suitability maps were prepared using Arc GIS software. About 48 per cent of the total area was moderate to marginally suitable and 13 per cent of the area was not suitable for both groundnut and finger millet. Lowland areas covering 12 per cent of the area was highly suitable, 15 per cent was moderate to marginally suitable and 20 per cent was not suitable for paddy cultivation.     

Approximating covariance matrices estimated in multivariate models by estimated auto- and cross-covariances

Quantities like tropospheric zenith delays or station coordinates are repeatedly measured at permanent VLBI or GPS stations so that time series for the quantities at each station are obtained. The covariances of these quantities can be estimated in a multivariate linear model. The covariances are needed for computing uncertainties of results derived from these quantities. The covariance matrix for many permanent stations becomes large, the need for simplifying it may therefore arise under the condition that the uncertainties of derived results still agree. This is accomplished by assuming that the different time series of a quantity like the station height for each permanent station can be combined to obtain one time series. The covariance matrix then follows from the estimates of the auto- and cross-covariance functions of the combined time series. A further approximation is found, if compactly supported covariance functions are fitted to an estimated autocovariance function in order to obtain a covariance matrix which is representative of different kinds of measurements. The simplification of a covariance matrix estimated in a multivariate model is investigated here for the coordinates of points of a grid measured repeatedly by a laserscanner. The approximations are checked by determining the uncertainty of the sum of distances to the points of the grid. To obtain a realistic value for this uncertainty, the covariances of the measured coordinates have to be considered. Three different setups of measurements are analyzed and a covariance matrix is found which is representative for all three setups. Covariance matrices for the measurements of laserscanners can therefore be determined in advance without estimating them for each application.     

星载方位多波束ScanSAR 成像

提出了一种新型星载方位多波束ScanSAR 工作模式, 实现了在保持方位向高分辨率的前提下对距离向 测绘带大幅扩展。分析了该工作方式的特点及主要相位误差, 给出了相应的误差补偿方法。介绍了该工作方式下回 波数据的模拟方法, 并讨论了几种适合该工作方式的成像算法。最后通过仿真对该工作方式的可行性及补偿方法的 有效性进行了验证。     

鲜水河断裂带的构造大地测量

利用短边GPS点阵、短基线标石阵和精密激光测距 ,在与鲜水河断裂带的断层破裂带垂直的剖面上和构造盆地内进行构造大地测量 ,获得了断层软弱带和断层蠕变带上的最佳形变分布。综合利用相关地学成果和理论 ,揭示了构造形变所隐含的动力学信息 ,识别了断层应变带、塑性流变带和主断面在地面的分布范围。用外围GPS远场联测成果探讨了该断裂带的驱动力机制 ,匡正了其“左旋”运动和现今活动速度的含义。最后 ,评估了该断裂的地震活动趋势     

利用参考图像的遥感图像模糊估计与恢复

提出一种新的利用参考图像的模糊估计与恢复方法。同一场景不同时相的图像,由于拍摄条件的不同,清晰化程度也往往不同,将高质量的图像作为参考,为模糊估计和恢复提供先验知识,在Bayes统一的框架内进行快速PSF估计,然后使用总变分最小化方法进行恢复。通过真实遥感图像实验,结果表明所提出的方法可以有效地估计退化模糊、恢复未失真图像。     

张量分类算法的遥感影像目标探测

提出了一种基于张量学习机的遥感影像目标探测方法。该方法基于张量数据模型和张量代数运算, 针对遥感影像数据多维或高维的特点, 将基于向量的监督法学习机扩展为基于张量的监督法学习机, 然后利用凸函数最优化理论和交互投影迭代法求得张量学习机的最优解。最后分别以高光谱遥感影像和高分辨率遥感影像为例, 使用张量学习机进行目标探测。实验表明, 与支持向量机等方法相比, 本文的方法在保持较高探测成功率的同时更好的抑制了虚警。